Inverse halftoning of error-diffused images using classified vector quantization and residual information

This paper extends and modifies Classified Vector Quantization (CVQ) to solve the problem of inverse halftoning. The proposed process consists of two phases: the encoding phase and decoding phase. The encoding procedure needs a codebook for the encoder, while the decoding process requires another codebook for the decoder. The difference between an input vector and its corresponding codeword is included to reconstruct a gray-scale image. The experiments show that our algorithm is robust to the filter which is used to generate an error-diffused image. Compared with other available techniques, our approach has the better image quality. The main contribution of this paper is that it opens another area of application for VQ.